Python Coding Interview Questions For Data Engineers
Python Coding Interview Questions For Data Engineers – A comprehensive guide to Python concepts and questions from top companies for all budding data engineers to help them prepare for their next interview.
Today we will cover interview questions on Python for data engineering interview. This article will cover the concepts and skills required in Python to conduct data engineering interviews. As a data engineer, you need to be really good at SQL and Python. This blog will only cover Python, but if you are interested in learning about SQL, there is a comprehensive article “Data Engineer Interview Questions”.
Python Coding Interview Questions For Data Engineers
Python tops the PYPL programming language popularity index, based on an analysis of how often tutorials for various programming languages are searched for on Google. Python is the best among many other languages for data engineering and data science.
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Data engineers usually work with various data formats and Python makes it easy to work with such formats. Also, data engineers must use APIs to retrieve data from various sources. Usually the data is in JSON format and Python makes it easy to work with JSON as well. Data engineers not only extract data from various sources but they are also responsible for data processing. One of the most famous data engine is Apache Spark, if you know Python, you can work very well with Apache Spark because they provide API for it. Python has become an essential skill for a data engineer in recent times! Now let’s see what makes a good data engineer.
Data engineering is a broad discipline with many different roles and responsibilities. The ultimate goal of data engineering is to have a continuous flow of data for the business that enables data-driven decision making in the organization and helps in machine learning for data scientists/analysts.
This data flow can be accomplished in many different ways and one way is to use Python.
In addition to technical skills, a good data engineer also has excellent communication skills. These skills are especially necessary when you explain concepts to a non-technical audience in a company. Now that you know what makes a good data engineer, let’s see how Python is used by data engineers.
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Now that you have an overview of what makes a good data engineer, let’s see how Python is used by data engineers and how important it is. One of the most important uses of Python for data engineers is building data and analytics pipelines. These pipelines take data from multiple sources and transform it into a usable format and drop it into a data lake or data warehouse so that data analysts and data scientists can consume the data.
Let’s move on to the Python interview questions! If you’re completely new to Python, here’s a hands-on course that will get you started. Otherwise, get started with these interview questions! You can practice them on your own machine, or you can check out the full list of interactive challenges.
The question asks you to find the highest paying job titles. In this question, there are two tables.
There are two tables; worker and title and they can be linked by both worker_id and worker_ref_id. To join tables in Pandas, make the column names consistent. Let’s rename the title table from worker_ref_id to worker_id using the ‘rename’ function.
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Using the pandas ‘merge’ function, join the two task ID tables to get IDs and titles in a single data frame. Save the join results in a separate data frame.
The query only asks for worker titles and thus, we will select only worker titles from the above data frame.
Below is the final code for this Python Data Engineer Interview Question. Note that you must import all relevant/required libraries above.
We need to find the email activity of each user and then sort all users in descending order according to their email activity. If there are users with similar email activity, then sort those users alphabetically.
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The first step is to count each user’s email activity. We will use the pandas group by function to group users and then count the number of emails sent by that user. The result will be a series, so we need to convert it to a data frame using the ‘to_frame’ function and then reset the indexes.
Then, sort the data by total emails sent in descending order and if two users have sent the same email, sort them by ‘from_user’ in ascending order. To incorporate this into the code, we’ll use sort_values , which will take two variables in the first argument and the ‘True/False’ values in the ‘ascending’ argument of the function.
Once we’ve sorted the data by email activity and user, it’s time to rank the data. This is similar to using the RANK() OVER() functions in SQL. Pandas has a function called ‘rank()’ which takes several arguments.
For the category() function, the first argument is a method that will equal ‘first’. There are many methods provided by the rank() function, you can check the details here. The “first” method specifies the categories to appear in the table, which suits our needs.
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Thus, we have a rank for each user based on total emails sent. If the number of e-mails sent is even, then we sort alphabetically by the field_user.
It looks like an event table where each row represents a specific user, the number of user actions and an action named action.
There are two tables; asana_users and asana_tables. So the first step is to join both tables on user_id.
We need to focus only on January 2022, so filter the data frame for it. We can use function in pandas ‘al_period’.
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After we’ve filtered for ClassPass and the month of January, it’s time to count the number of actions for each user. You can use pandas groupby function.
In this Python Data Engineer interview question, we have to count the number of street names for each zip code with some conditions given in the question. For example, we need to count only the first part of the name if the street name has multiple words (pre-main can be counted as ex).
We need to split the business address to get the first part of the street name. For example, the street name ‘350 Broadway St’ can be split into different elements using the split function. Let’s use a list comprehension (Lambda function) to create a function on the go.
The output of the above code will give a list of elements. The string will be split into multiple elements depending on the type of delimiter used. In this case, we used ‘space’ as the delimiter and thus, ‘350 Broadway St’ will be converted to a list like [ “350”, “Broadway”, “St” ].
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The question mentions uppercase street names so we should lower() or super() all street names. Let’s use lowercase letters for this question. Also, the first element of the list must be a word so if the first element is a number, then take the next element. Below is the implemented code for this.
Now find the number of non-unique street names. After the group by function, the data is converted into a series and hence, we need the to_frame function to convert the data into a frame. Name the number of streets as ‘n_street’ and sort them in descending order using the sort_values function.
First, it is an event table without any unique identifier such as event_id. Then remove duplicates from this table if any using the drop_duplicates() function in pandas.
In this table, we have user id, account id and dates but we need only two columns as per query; User IDs and dates. Let’s select just those two columns and sort them in ascending order using pandas’ sort_values() function for each user and each date.
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In the next step, you need to think of a function similar to LEAD() or LAG() in SQL. There is a function called SHIFT() which can help us move dates. This function takes a scalar parameter called period, which represents the number of moves to be made along the desired axis. This feature is very useful when dealing with time-series data. Let’s use this function and create another column by adding 2 more days to it because we need users who are active for 3 consecutive days.
Now that we have the data in the required format, let’s compare the ‘3_days’ and ‘shift_3’ columns and see if they are equal. If they are equal, we extract the corresponding user ID because it is asking the question.
A table is available to solve this question. In this question, we need to rank the hosts by number
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